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Journal Abstract Search
250 related items for PubMed ID: 31313129
1. An efficient heart murmur recognition and cardiovascular disorders classification system. Ahmad MS, Mir J, Ullah MO, Shahid MLUR, Syed MA. Australas Phys Eng Sci Med; 2019 Sep; 42(3):733-743. PubMed ID: 31313129 [Abstract] [Full Text] [Related]
5. Automatic heart sound classification from segmented/unsegmented phonocardiogram signals using time and frequency features. Khan FA, Abid A, Khan MS. Physiol Meas; 2020 Jun 03; 41(5):055006. PubMed ID: 32259811 [Abstract] [Full Text] [Related]
6. Classification of heart murmurs using cepstral features and support vector machines. Vepa J. Annu Int Conf IEEE Eng Med Biol Soc; 2009 Jun 03; 2009():2539-42. PubMed ID: 19964976 [Abstract] [Full Text] [Related]
16. Unsupervised detection and classification of heartbeats using the dissimilarity matrix in PCG signals. Torre-Cruz J, Martinez-Muñoz D, Ruiz-Reyes N, Muñoz-Montoro AJ, Puentes-Chiachio M, Canadas-Quesada FJ. Comput Methods Programs Biomed; 2022 Jun 03; 221():106909. PubMed ID: 35649297 [Abstract] [Full Text] [Related]
17. An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT). Jamil S, Roy AM. Comput Biol Med; 2023 May 03; 158():106734. PubMed ID: 36989745 [Abstract] [Full Text] [Related]
18. HBNET: A blended ensemble model for the detection of cardiovascular anomalies using phonocardiogram. Netto AN, Abraham L, Philip S. Technol Health Care; 2024 May 03; 32(3):1925-1945. PubMed ID: 38393859 [Abstract] [Full Text] [Related]
19. Machine Learning Algorithms for Processing and Classifying Unsegmented Phonocardiographic Signals: An Efficient Edge Computing Solution Suitable for Wearable Devices. De Fazio R, Spongano L, De Vittorio M, Patrono L, Visconti P. Sensors (Basel); 2024 Jun 14; 24(12):. PubMed ID: 38931636 [Abstract] [Full Text] [Related]